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Systems biology of platelet-vessel wall interactions

Blood systems biology seeks to quantify outside-in signaling as platelets respond to numerous external stimuli, typically under flow conditions. Platelets can activate via GPVI collagen receptor and numerous G-protein coupled receptors (GPCRs) responsive to ADP, thromboxane, thrombin, and prostacycl...

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Autores principales: Diamond, Scott L., Purvis, Jeremy, Chatterjee, Manash, Flamm, Matthew H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3752459/
https://www.ncbi.nlm.nih.gov/pubmed/23986721
http://dx.doi.org/10.3389/fphys.2013.00229
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author Diamond, Scott L.
Purvis, Jeremy
Chatterjee, Manash
Flamm, Matthew H.
author_facet Diamond, Scott L.
Purvis, Jeremy
Chatterjee, Manash
Flamm, Matthew H.
author_sort Diamond, Scott L.
collection PubMed
description Blood systems biology seeks to quantify outside-in signaling as platelets respond to numerous external stimuli, typically under flow conditions. Platelets can activate via GPVI collagen receptor and numerous G-protein coupled receptors (GPCRs) responsive to ADP, thromboxane, thrombin, and prostacyclin. A bottom-up ODE approach allowed prediction of platelet calcium and phosphoinositides following P2Y(1) activation with ADP, either for a population average or single cell stochastic behavior. The homeostasis assumption (i.e., a resting platelet stays resting until activated) was particularly useful in finding global steady states for these large metabolic networks. Alternatively, a top-down approach involving Pairwise Agonist Scanning (PAS) allowed large data sets of measured calcium mobilization to predict an individual's platelet responses. The data was used to train neural network (NN) models of signaling to predict patient-specific responses to combinatorial stimulation. A kinetic description of platelet signaling then allows prediction of inside-out activation of platelets as they experience the complex biochemical milieu at the site of thrombosis. Multiscale lattice kinetic Monte Carlo (LKMC) utilizes these detailed descriptions of platelet signaling under flow conditions where released soluble species are solved by finite element method and the flow field around the growing thrombus is updated using computational fluid dynamics or lattice Boltzmann method. Since hemodynamic effects are included in a multiscale approach, thrombosis can then be predicted under arterial and venous thrombotic conditions for various anatomical geometries. Such systems biology approaches accommodate the effect of anti-platelet pharmacological intervention where COX1 pathways or ADP signaling are modulated in a patient-specific manner.
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spelling pubmed-37524592013-08-28 Systems biology of platelet-vessel wall interactions Diamond, Scott L. Purvis, Jeremy Chatterjee, Manash Flamm, Matthew H. Front Physiol Physiology Blood systems biology seeks to quantify outside-in signaling as platelets respond to numerous external stimuli, typically under flow conditions. Platelets can activate via GPVI collagen receptor and numerous G-protein coupled receptors (GPCRs) responsive to ADP, thromboxane, thrombin, and prostacyclin. A bottom-up ODE approach allowed prediction of platelet calcium and phosphoinositides following P2Y(1) activation with ADP, either for a population average or single cell stochastic behavior. The homeostasis assumption (i.e., a resting platelet stays resting until activated) was particularly useful in finding global steady states for these large metabolic networks. Alternatively, a top-down approach involving Pairwise Agonist Scanning (PAS) allowed large data sets of measured calcium mobilization to predict an individual's platelet responses. The data was used to train neural network (NN) models of signaling to predict patient-specific responses to combinatorial stimulation. A kinetic description of platelet signaling then allows prediction of inside-out activation of platelets as they experience the complex biochemical milieu at the site of thrombosis. Multiscale lattice kinetic Monte Carlo (LKMC) utilizes these detailed descriptions of platelet signaling under flow conditions where released soluble species are solved by finite element method and the flow field around the growing thrombus is updated using computational fluid dynamics or lattice Boltzmann method. Since hemodynamic effects are included in a multiscale approach, thrombosis can then be predicted under arterial and venous thrombotic conditions for various anatomical geometries. Such systems biology approaches accommodate the effect of anti-platelet pharmacological intervention where COX1 pathways or ADP signaling are modulated in a patient-specific manner. Frontiers Media S.A. 2013-08-26 /pmc/articles/PMC3752459/ /pubmed/23986721 http://dx.doi.org/10.3389/fphys.2013.00229 Text en Copyright © 2013 Diamond, Purvis, Chatterjee and Flamm. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Diamond, Scott L.
Purvis, Jeremy
Chatterjee, Manash
Flamm, Matthew H.
Systems biology of platelet-vessel wall interactions
title Systems biology of platelet-vessel wall interactions
title_full Systems biology of platelet-vessel wall interactions
title_fullStr Systems biology of platelet-vessel wall interactions
title_full_unstemmed Systems biology of platelet-vessel wall interactions
title_short Systems biology of platelet-vessel wall interactions
title_sort systems biology of platelet-vessel wall interactions
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3752459/
https://www.ncbi.nlm.nih.gov/pubmed/23986721
http://dx.doi.org/10.3389/fphys.2013.00229
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